Project Summary Recent cancer genome sequencing efforts have determined the complete protein coding regions for thousands of patients across tens of different cancer types. Initial analyses have revealed that cancer genomes can have numerous genetic alterations, but only a subset are thought to be important for cancer initiation or progression. Further, across patients, there is a high degree of mutational heterogeneity with very few genes altered in a high fraction of cases, and many infrequently altered genes, some of which are functionally important in cancer cells. These factors significantly complicate efforts to identify cancer-related genes. Our long-term goal is to identify cancer-related genes by analyzing the genomes of cohorts of individuals with a particular cancer. The key insight underlying our work is that molecular interactions and networks reveal important aspects of protein functioning, and thus provide an important context by which to tackle the mutational heterogeneity observed across cancers. Our specific aims are: (1) To develop structure-based methods that uncover proteins enriched in somatic mutations in their interaction interfaces, as mutations in these sites are likely to affect protein functioning. (2) To develop network-based methods for de novo discovery of pathways that are mutated across patient samples, as mutations in cancers tend to target specific pathways?even if different genes within them are mutated in different individuals?and genes proximal in networks tend to be functionally related. (3) To develop metabolite- centric methods that use protein-small molecule networks in order to uncover mutated proteins that alter cellular metabolism, as reprogrammed metabolism is increasingly being recognized as a major adaptation of cancer cells. By pursuing these three complementary and tightly coupled aims?which exploit critical but often overlooked structural and network information?we will vastly advance the state-of-the-art in computational methods for analyzing cancer genomes. These analyses will deepen our understanding of cancer biology, and will ultimately lead to better patient stratification, refined prognostic tools, and novel therapeutics. .